def recall_female(y_true, y_pred):
""" Compute recall for the class "female"
:param y_true: true labels (dummy numpy array, column 0 for male, column 1 for female)
:param y_pred: predicted labels (dummy numpy array, column 0 for male, column 1 for female)
:return: recall (float)
"""
nb_female = K.sum(K.round(K.clip(y_true[:, 1], 0, 1)))
male_true_positives = K.sum(
K.round(K.clip(y_true[:, 1] * y_pred[:, 1], 0, 1)))
recall = male_true_positives / (nb_female + K.epsilon())
return recall
def precision_male(y_true, y_pred):
""" Compute precision for the class "male"
:param y_true: true labels (dummy numpy array, column 0 for male, column 1 for female)
:param y_pred: predicted labels (dummy numpy array, column 0 for male, column 1 for female)
:return: precision (float)
"""
nb_male_pred = K.sum(K.round(K.clip(y_pred[:, 0], 0, 1)))
male_true_positives = K.sum(
K.round(K.clip(y_true[:, 0] * y_pred[:, 0], 0, 1)))
precision = male_true_positives / (nb_male_pred + K.epsilon())
return precision
def binary_accuracy(y_true, y_pred):
return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)
def binary_accuracy(y_true, y_pred): return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)
def iou_accuracy(y_true, y_pred): i = K.cast(K.cumsum(K.maximum(y_true*K.round(y_pred), 0.)), K.floatx()) u = K.cast(K.cumsum(K.maximum(y_true+K.round(y_pred), 0.)), K.floatx()) return i/u
